On the Fundamental Limit of Orthogonal Matching Pursuit for Multiple Measurement Vector

被引：17
|
作者：
Li, Haifeng [1 ]
Wang, Libo [2 ]
Zhan, Xueqiu [3 ]
Jain, Deepak Kumar [4 ]
机构：
[1] Henan Normal Univ, Coll Math & Informat Sci, Xinxiang 453007, Peoples R China
[2] Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510632, Guangdong, Peoples R China
[3] Wuxi Inst Technol, Dept Basic Sci, Wuxi 214121, Peoples R China
[4] Chongqing Univ Posts & Telecommun, Coll Automat, Key Lab Intelligent Air Ground Cooperat Control U, Chongqing 400065, Peoples R China
来源：
IEEE ACCESS | 2019年 / 7卷
基金：
中国国家自然科学基金;
关键词：
Compressed sensing; multiple measurement vector; greedy algorithm; RECOVERY;
D O I：
10.1109/ACCESS.2019.2907684
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Given Y is an element of R-mxl and A is an element of R-mxn, the multiple measurement vector (MMV) problem aims to recover the support of the K-row sparse matrix X from Y = AX + E. In this paper, we present a sufficient condition for exact support recovery of K-row sparse matrices via the orthogonal matching pursuit for MMV (OMPMMV) algorithm in the noisy case. Furthermore, in the noiseless case, our proposed condition is optimal.
引用
收藏
页码：48860 / 48866
页数：7
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